Automated Scene Understanding

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$739,954.00
Award Year:
2012
Program:
SBIR
Phase:
Phase II
Contract:
N00014-12-C-0263
Award Id:
n/a
Agency Tracking Number:
O2-1225
Solicitation Year:
2010
Solicitation Topic Code:
OSD10-L04
Solicitation Number:
2010.2
Small Business Information
11600 Sunrise Valley Drive, Suite # 290, Reston, VA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
038732173
Principal Investigator:
Atul Kanaujia
Principal Investigator
(703) 654-9300
akanaujia@objectvideo.com
Business Contact:
Paul Brewer
VP, New Technology
(703) 725-3084
pbrewer@objectvideo.com
Research Institute:
Stub




Abstract
Automatic extraction and representation of visual concepts and semantic information in scenes is a desired capability in surveillance operations. In this effort we will advance the foundations of data representation and fusion at various levels of abstraction. We target the problem of complex event recognition in network information environment, where lack of effective visual processing tools and incomplete domain knowledge frequently cause uncertainty in the datasets and consequently, in the visual primitives extracted from it. We employ Markov Logic Network (MLN) to address the task of reasoning under uncertainty. In Phase I, we demonstrated use of MLN as a domain knowledge representation language that can be used for inferring complex events in real world. In Phase II, our emphasis will be on developing algorithms to fuse data from multiple sources, perform reasoning in the presence of incomplete data, and transfer learning for domain adaptation. At the visual processing level, transfer learning will enable zero-shot recognition of unknown classes. At the decision level, transfer learning is applied to MLN to automatically infer rules for related but unseen domains. Technical claims made during the study will be justified using rigorous testing and comparison with other state-of-the-art methods on publically available datasets.

* information listed above is at the time of submission.

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